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Digital Image Representation by Atomic Functions: The Compression and Protection of Data for Edge Computing in IoT

Viktor Makarichev¹, Vladimir Lukin², Oleg Illiashenko¹

¹Department of Computer Systems, Networks and Cybersecurity, National Aerospace University "KhAI", 17, Chkalov Str., 61070 Kharkiv, Ukraine.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

A new discrete atomic transform (DAT) offers efficient lossless image compression and data protection for edge computing in Internet of Things (IoT) systems. This method reduces memory costs and enhances security for industrial applications like autonomous transport.

Keywords:

IoT atomic function atomic wavelet discrete atomic transform edge computing image protection image representation lossless image compression privacy security

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Area of Science:

Computer Science
Image Processing
Data Security

Background:

Digital images are crucial for various sectors, but large datasets demand efficient storage and processing, especially with edge computing (EC) in Internet of Things (IoT) systems.
Industrial IoT applications, such as autonomous transport, face challenges in secure and low-resource image processing.
Existing methods struggle to balance compression, protection, and low-complexity analysis for EC environments.

Purpose of the Study:

To develop a novel image representation method for EC-based IoT systems that integrates compression and protection.
To address the need for low-complexity image analysis, secure storage, and privacy in industrial IoT.
To propose and analyze a discrete atomic transform (DAT) for enhanced image processing and security.

Main Methods:

Development of a lossless image compression algorithm utilizing discrete atomic transform (DAT).
Investigation of DAT's performance and compression efficiency (CR) across different DAT structures.
Proposal and analysis of a multi-level data structure for storing compressed and protected image data.

Main Results:

The developed DAT-based algorithm achieves efficient lossless compression with low computational complexity.
Significant reduction in memory expenses was demonstrated.
The study confirmed that DAT structure variations have a minor impact on compression ratio, suggesting potential for data protection.

Conclusions:

The proposed DAT-based image representation offers a viable solution for secure, compressed, and efficiently processed images in EC-IoT systems.
The algorithm provides a balance of compression, protection, and low complexity suitable for industrial domains.
The findings support the application of DAT for data protection and security assurance in edge computing environments.